"Thinking in pictures," [1] i.e., spatial-temporal reasoning, effortless and
instantaneous for humans, is believed to be a significant ability to perform
logical induction and a crucial factor in the intellectual history of
technology development. Modern Artificial Intelligence (AI), fueled by massive
datasets, deeper models, and mighty computation, has come to a stage where
(super-)human-level performances are observed in certain specific tasks.
However, current AI's ability in "thinking in pictures" is still far lacking
behind. In this work, we study how to improve machines' reasoning ability on
one challenging task of this kind: Raven's Progressive Matrices (RPM).
Specifically, we borrow the very idea of "contrast effects" from the field of
psychology, cognition, and education to design and train a
permutation-invariant model. Inspired by cognitive studies, we equip our model
with a simple inference module that is jointly trained with the perception
backbone. Combining all the elements, we propose the Contrastive Perceptual
Inference network (CoPINet) and empirically demonstrate that CoPINet sets the
new state-of-the-art for permutation-invariant models on two major datasets. We
conclude that spatial-temporal reasoning depends on envisaging the
possibilities consistent with the relations between objects and can be solved
from pixel-level inputs.

本文探讨了如何通过心理学、认知科学和教育学的思想来设计和训练一个置换不变的模型，以提高机器在空间 / 时间推理方面的推理能力。通过 Contrastive Perceptual Inference network (CoPINet) 的实验，我们证明了它在两个主要数据集上设置了新的技术水平，从而提高了机器在思维空间 / 时间推理方面的能力。